What are Sankey Plots?¶
- Flow diagram
- Width of flows = amount
- Named after Matthew Henry Phineas Riall Sankey
- Flow and distribution of heat in steam engines, 1898
https://upload.wikimedia.org/wikipedia/commons/1/10/JIE_Sankey_V5_Fig1.png
History¶
- First and most famous Sankey plot: Charles Minard's Map of Napoleon's Russian Campaign of 1812
- Sankey diagram on map
- Created 1869
- Many data types
https://upload.wikimedia.org/wikipedia/commons/2/29/Minard.png
How are they used?¶
- Energy: input, output, waste
- International Energy Agency: Flow of energy for the entire planet from 1973 to 2019
- Interactive: Show change of energy flow for selected country and years
https://www.iea.org/sankey/#?c=World&s=Balance
How are they used?¶
- Eurostat: Interactive energy balance flow for EU or countries of EU from 1990 to 2020
https://ec.europa.eu/eurostat/web/energy/energy-flow-diagrams
How are they used?¶
- Vote flows in elections
https://www.tagesschau.de/inland/btw21/waehlerwanderung-bundestagswahl-103.html
How are they used?¶
- Vote flows in elections
https://www.tagesschau.de/inland/btw21/waehlerwanderung-bundestagswahl-103.html
How are they used?¶
- Vote flows in elections
https://www.tagesschau.de/inland/btw21/waehlerwanderung-bundestagswahl-103.html
How are they used?¶
- Vote flows in elections
https://www.economist.com/graphic-detail/2019/11/01/a-british-election-and-other-uncertainties
Wrap Up¶
- Show flow of different categories between two or more steps
- Start: Initial distribution of categories (usually the left side)
- Flow: Redistribution between steps (usually from left to right)
- End: New distribution (usually the right side)
- Distributions can be calculated with flow data
- Width of lines represent the volume or amount
Python libraries for Sankey plots¶
Data¶
- Berlin census from https://www.statistik-berlin-brandenburg.de/a-i-3-j
https://download.statistik-berlin-brandenburg.de/0c8e82331bc2327a/802f7f020114/SB_A01-03-00_2020j01_BE.xlsx
In [2]:
data = {
'2020': {
# inputs
'start': 3669491, # census at start of the year
'births': 38693,
'immigration': 142923,
# outputs
'deaths': -37642,
'emmigration': -144881,
'error': -4496,
'end': -3664088 # census at the end of the year
}
}
flows = list(data['2020'].values())
labels = list(data['2020'].keys())
flows, labels
Out[2]:
matplotlib¶
In [3]:
import matplotlib.pyplot as plt
from matplotlib.sankey import Sankey
In [4]:
sankey = Sankey() # init
sankey.add(flows=flows, labels=labels) # add flow(s)
sankey.finish() # create
plt.show() # show
In [5]:
scale = 0.0000001
sankey = Sankey(scale=scale) # init with scale!
sankey.add(flows=flows, labels=labels)
sankey.finish()
plt.show()
In [6]:
sankey = Sankey(scale=scale)
# 0 (inputs from the left, outputs to the right),
# 1 (from and to the top) or -1 (from and to the bottom).
orientations = [0, -1, 1, -1, 1, -1, 0]
# add flow(s) with orientations
sankey.add(flows=flows, labels=labels, orientations=orientations)
sankey.finish()
plt.show()
In [7]:
pathlengths=[0.1, 0.1, 0.1, 0.1, 0.1, 0.3, 0.1]
sankey = Sankey(scale=scale)
sankey.add(
flows=flows, labels=labels,
orientations=orientations,
pathlengths=pathlengths,
) # add flow(s) with orientations and pathlengths
sankey.finish()
plt.show()
In [8]:
def format_number(n):
return '{:,}'.format(abs(n)) # add thousand separator
# add number format
sankey = Sankey(scale=scale, format=format_number)
sankey.add(
flows=flows, labels=labels,
orientations=orientations,
pathlengths=pathlengths,
)
sankey.finish()
plt.show()
In [9]:
sankey = Sankey(scale=scale, format=format_number)
sankey.add(
flows=flows, labels=labels,
orientations=orientations,
pathlengths=pathlengths,
facecolor='lightgray' # change color
)
sankey.finish()
plt.title("Berlin Census 2020") # add title
plt.show()
In [10]:
# add second year
data = {
'2019': {
'start 2019': 3644826,
'births': 39503,
'immigration': 184744,
'deaths': -34739,
'emmigration': -161513,
'error': -3330,
'end 2019': -3669491
},
'2020': {
'start 2020': 3669491,
'births': 38693,
'immigration': 142923,
'deaths': -37642,
'emmigration': -144881,
'error': -4496,
'end 2020': -3664088
}
}
In [11]:
flows_2019 = list(data['2019'].values())
labels_2019 = list(data['2019'].keys())
labels_2019[-1] = None # remove last label
flows_2020 = list(data['2020'].values())
labels_2020 = list(data['2020'].keys())
In [12]:
pathlengths=[0.3, 0.3, 0.1, 0.1, 0.3, 0.5, 0.3] # new pathlengths
sankey = Sankey(scale=scale, format=format_number)
sankey.add( # add 2019
flows=flows_2019, labels=labels_2019,
orientations=orientations,
pathlengths=pathlengths,
facecolor='lightgray'
)
sankey.add( # add 2020
flows=flows_2020, labels=labels_2020,
orientations=orientations,
pathlengths=pathlengths,
prior=0, connect=(len(flows_2019)-1, 0), # connect second flow to first
facecolor='darkgray'
)
sankey.finish()
plt.title("Berlin Census 2019 & 2020") # add title
plt.show()
matplotlib notes¶
- https://matplotlib.org/stable/gallery/specialty_plots/sankey_basics.html
- close to the original flow diagram
- do everything explicitly
- good (extensive) documentation
- Pitfalls
scaleargument for large/small numbersformatdifficult to handle- ...
pySankey¶
In [13]:
import pandas as pd
from pySankey.sankey import sankey
In [14]:
# create DataFrame from 2020 data
df_2020 = pd.DataFrame([
# start -> deaths
{'source': 'start', 'target': 'deaths', 'value': 37642},
# start -> emmigration
{'source': 'start', 'target': 'emmigration', 'value': 144881},
# start -> error
{'source': 'start', 'target': 'error', 'value': 4496},
# start -> end
{'source': 'start', 'target': 'end', 'value': 3669491},
# births -> end
{'source': 'births', 'target': 'end', 'value': 38693},
# immigration -> end
{'source': 'immigration', 'target': 'end', 'value': 142923},
])
df_2020
Out[14]:
In [15]:
sankey(
left=df_2020['source'], right=df_2020['target'],
leftWeight=df_2020['value'],
fontsize=14,
#figure_name="Berlin Census 2020", # used for saving png, not title
)
pySankey notes¶
- https://github.com/anazalea/pySankey
- only code documentation
- examples do not work after pip install
figure_namenot in docstring: used for saving file (not title)- try & error
- only one flow step possible
- looks more like a modern Sankey plot than matplotlib
- great for simple plots
psankey¶
In [16]:
from psankey.sankey import sankey
In [17]:
nodes, fig, ax = sankey(
df_2020, aspect_ratio=4/3,
nodelabels=True, linklabels=True, labelsize=5,
)
plt.title("Berlin Census 2020") # add title
plt.show()
In [18]:
# create DataFrame from 2019 & 2020 data
df = pd.DataFrame([
# 2019
{'source': '2019', 'target': '2020', 'value': 3644826},
{'source': '2019', 'target': 'deaths `19', 'value': 34739},
{'source': '2019', 'target': 'emmigration `19', 'value': 161513},
{'source': '2019', 'target': 'error `19', 'value': 3330},
{'source': 'births `19', 'target': '2020', 'value': 39503},
{'source': 'immigration `19', 'target': '2020', 'value': 184744},
# 2020
{'source': '2020', 'target': '2021', 'value': 3669491},
{'source': '2020', 'target': 'deaths `20', 'value': 37642},
{'source': '2020', 'target': 'emmigration `20', 'value': 144881},
{'source': '2020', 'target': 'error `20', 'value': 4496},
{'source': 'births `20', 'target': '2021', 'value': 38693},
{'source': 'immigration `20', 'target': '2021', 'value': 142923},
])
df.head(3)
Out[18]:
In [19]:
nodes, fig, ax = sankey(
df, aspect_ratio=4/3,
nodelabels=True, linklabels=True, labelsize=5,
)
plt.title("Berlin Census 2019 & 2020") # add title
plt.show()
psankey notes¶
- https://github.com/mandalsubhajit/psankey
- works with
pd.DataFrames - works for multiple flow steps
- short but helpful documentation in
README.md - some smart options
nodemodifierto highlight nodes
- node positions not customizable
holoviews¶
In [20]:
import holoviews as hv
from holoviews import opts, dim
hv.extension('bokeh')
width, height = 600, 400
In [21]:
# run example code
sankey = hv.Sankey([
['A', 'X', 5], ['A', 'Y', 7], ['A', 'Z', 6],
['B', 'X', 2], ['B', 'Y', 9], ['B', 'Z', 4]
])
sankey.opts(width=width, height=height)
Out[21]:
In [22]:
df_2020.head()
Out[22]:
In [23]:
# pass DataFrame from previous example
sankey = hv.Sankey(df_2020)
sankey.opts(width=width, height=height)
Out[23]:
In [24]:
df.head()
Out[24]:
In [25]:
sankey = hv.Sankey(df)
sankey.opts(width=width, height=height, cmap='Set2',
edge_color=dim('source').str(),
node_color=dim('target').str())
Out[25]:
holoviews notes¶
- https://holoviews.org/reference/elements/bokeh/Sankey.html
- easy to get started
- interactive plot (bokeh backend)
- short user guide
- fixed node position
- changing colors required
cmap - only few examples
plotly¶
In [26]:
import plotly.graph_objects as go
In [27]:
# example from https://plotly.com/python/sankey-diagram/
fig = go.Figure(data=[go.Sankey(
node = dict(
pad = 15,
thickness = 20,
line = dict(color="black", width=0.5),
label = ["A1", "A2", "B1", "B2", "C1", "C2"],
color = "blue"
),
link = dict(
# indices correspond to labels, eg A1, A2, A1, B1, ...
source = [0, 1, 0, 2, 3, 3],
target = [2, 3, 3, 4, 4, 5],
value = [8, 4, 2, 8, 4, 2]
))])
In [28]:
fig.update_layout(
title_text="Basic Sankey Diagram",
width=width, height=height, font_size=10)
fig.show()
In [29]:
# create DataFrame from 2019 & 2020 data
s, t, v, c = 'source', 'target', 'value', 'color'
df = pd.DataFrame([
# 2019
{s: '2019', t: '2020', v: 3644826, c: 'lightgray'},
{s: '2019', t: 'deaths `19', v: 34739, c: '#a6cee3'},
{s: '2019', t: 'emmigration `19', v: 161513, c: '#1f78b4'},
{s: '2019', t: 'error `19', v: 3330, c: '#f1b6da'},
{s: 'births `19', t: '2020', v: 39503, c: '#b2df8a'},
{s: 'immigration `19', t: '2020', v: 184744, c: '#33a02c'},
# 2020
{s: '2020', t: '2021', v: 3669491, c: 'lightgray'},
{s: '2020', t: 'deaths `20', v: 37642, c: '#a6cee3'},
{s: '2020', t: 'emmigration `20', v: 144881, c: '#1f78b4'},
{s: '2020', t: 'error `20', v: 4496, c: '#f1b6da'},
{s: 'births `20', t: '2021', v: 38693, c: '#b2df8a'},
{s: 'immigration `20', t: '2021', v: 142923, c: '#33a02c'},
])
df.head(3)
Out[29]:
In [30]:
# create nodes with index from DataFrame
# https://stackoverflow.com/a/69464558
import numpy as np
nodes = np.unique(df[["source", "target"]], axis=None)
nodes = pd.Series(index=nodes, data=range(len(nodes)))
nodes
Out[30]:
In [31]:
fig = go.Figure(
data=[
go.Sankey(
node={
"label": nodes.index,
},
link={
"source": nodes.loc[df["source"]],
"target": nodes.loc[df["target"]],
"value": df["value"],
})
]
)
In [32]:
fig.update_layout(
title_text="Berin Census 2019 & 2020",
width=width, height=height, font_size=10)
fig.show()
In [33]:
# create x, y and colors for the nodes
x = [.1, .4, .7, # years
.1, .4, # births
.3, .6, # deaths
.3, .6, # emmigration
.3, .6, # error
.1, .4, # immigration
]
y = [.5, .5, .5, # years
.75, .8, # births
.2, .25, # deaths
.25, .3, # emmigration
.15, .2, # error
.7, .75, # immigration
]
color = ["darkgray", "darkgray", "darkgray",
"#b2df8a", "#b2df8a", # light green
"#a6cee3", "#a6cee3", # light blue
"#1f78b4", "#1f78b4", # dark blue
'#f1b6da', '#f1b6da', # light pink
"#33a02c", "#33a02c", # dark green
]
x, y
Out[33]:
In [34]:
fig = go.Figure(
data=[
go.Sankey(
arrangement = "freeform",
node={
"label": nodes.index,
"x": x,
"y": y,
"pad": 100, # padding between nodes,
"color": color,
},
link={
"source": nodes.loc[df["source"]],
"target": nodes.loc[df["target"]],
"value": df["value"],
"color": df["color"],
})
]
)
In [35]:
fig.update_layout(
title_text="Berin Census 2019 & 2020", font_size=10
)
fig.show()
plotly notes¶
- https://plotly.com/python/sankey-diagram/
- very adjustable
- interactive
- nodes can be moved
- many examples
- difficult arrangement but possible!
Takeaways¶
- What do you want to show?
- Are you interested in the flows or distributions?
- Check if your data fits for sankey
- only distributions is not enough
- you need data for the flows
- not too many categories
- Make sure it is worth the effort
- Get data into the correct format
- Customizing the plot
- Think about colors!
- Search for good examples to guide you
Do¶
https://www.ipoint-systems.com/blog/from-data-to-knowledge-the-power-of-elegant-sankey-diagrams/
Sources and links to more material¶
- https://towardsdatascience.com/the-what-why-and-how-of-sankey-diagrams-430cbd4980b5
- https://www.sankey-diagrams.com/
- https://onlinelibrary.wiley.com/doi/10.1111/j.1530-9290.2008.00004.x
- The Sankey Diagram in Energy and Material Flow Management (Mario Schmidt): https://doi.org/10.1111%2Fj.1530-9290.2008.00004.x
- https://flothesof.github.io/sankey-tutorial-matplotlib.html
- https://www.ipoint-systems.com/blog/from-data-to-knowledge-the-power-of-elegant-sankey-diagrams/
